Analyzing Interpersonal Empathy via Collective Impressions

This paper presents a research framework for understanding the empathy that arises between people while they are conversing. By focusing on the process by which empathy is perceived by other people, this paper aims to develop a computational model that automatically infers perceived empathy from participant behavior. To describe such perceived empathy objectively, we introduce the idea of using the collective impressions of external observers. In particular, we focus on the fact that the perception of other's empathy varies from person to person, and take the standpoint that this individual difference itself is an essential attribute of human communication for building, for example, successful human relationships and consensus. This paper describes a probabilistic model of the process that we built based on the Bayesian network, and that relates the empathy perceived by observers to how the gaze and facial expressions of participants co-occur between a pair. In this model, the probability distribution represents the diversity of observers' impression, which reflects the individual differences in the schema when perceiving others' empathy from their behaviors, and the ambiguity of the behaviors. Comprehensive experiments demonstrate that the inferred distributions are similar to those made by observers.